We compare to the original rationalization technique Rnp [1]}, and several published models that achieve state-of-the-art results on real-world benchmarks, which include 3Player [2]}, HardKumahttps://github.com/bastings/interpretable_predictions. [3]}, and BERT-Rnp [4]}. 3Player model builds upon the original Rnp and encourages the completeness of rationale selection. HardKuma is a token-level method that optimizes the dependent selection of Rnp to encourage more human-interpretable extractions. BERT-Rnp re-implements the original Rnp with more powerful BERT generator and predictor.
Rnp is our main baseline to directly compare with, as Rnp and our A2r match in granularity of selection, optimization algorithm and model architecture. We include the other baselines to show the competitiveness of our A2r .
